Price of Robustness Optimization through Demand Forecasting and Robust Planning Models Integration in Waste Management

Robust optimization can be effectively used to protect production plans against uncertainties. This is particularly important in sectors where variability is inherent the process that must be optimally planned. The drawback is that, in real situations, some information can be added in order to better control the extra-cost resulting from considering the parameter variability. This work investigates how demand forecasting can be used in conjunction with robust optimization in order to achieve an optimal planning considering demand uncertainties. In the proposed procedure forecast is used to update uncertain parameters of the robust model. Moreover the robustness budget is optimized at each planned stage in a rolling planning horizon. In this way the parameters of the robust model can be dynamically updated tacking information from the data. The study is applied to a reverse logistics case, where the planning of sorting for material recycling is affected by uncertainties in the demand, consisting of the waste material to be sorted and recycled. Results are compared with the standard robust optimization approach, using real case instances, showing potentialities of the proposed method.

Rolling Supply Chain Scheduling considering Suppliers, Production, and Delivery Lot-Size

Supply chain management and integration play a key factor in contemporary manufacturing concept. Companies seek to integrate itself within a cooperative and mutual benefiting supply chain. Supply chain scheduling, as an important aspect of supply chain management, highly emphasizes on minimizing stock costs and delivery costs. Most previous researches on supply chain scheduling problems assume make-to-order production, which includes delivery cost in lot-size. This practice simplifies the complexity of the problem. Instead, this research discusses make-to-contract production, where the supply chain has a rolling planning horizon that changes according to contracts. Within a planning horizon, two types of interval are defined. The first is frozen interval, in which the manufacturing decision cannot be changed. The second is free interval, where schedules can be adjusted depending on new contracts. This research aims to build a robust rolling supply management schedule to satisfy customers’ needs, by considering supplier, production, and delivery lot-size simultaneously. The objective is to effectively decide a combination of supplier, production, and delivery lot-size that minimizes total cost consisting of supplier cost, finish good stock cost, and delivery cost. Based on the concept, this study designs a problem-solving process that combines the methods of rolling planning horizon and genetic algorithm. Delivery size (DS), finish good stock (FS), and early delivery cost (ED) are the three methods applied; each will provide a guideline to produce a feasible solution. By further considering the fluctuations in practical needs and performing an overall evaluation, a robust and optimal supply chain scheduling plan can be decided, including the optimal lot-sizes of supplier, production, and delivery. In the effectiveness test which considers 3 types of customer demands and 11 types of company cost structures, the simulated data test results suggest that the proposed methods in this study have excellent performance.